A Risk Prediction Model for Progression-Free Survival in Endometrial Cancer Integrating Clinicopathological Variables...

To develop and validate a prediction model for progression-free survival (PFS) in endometrial cancer (EC) using routine clinicopathological and preoperative laboratory data, and assess the incremental prognostic value of inflammatory, nutritional, and tumor markers.

This single-center retrospective cohort study included 282 EC patients receiving initial treatment. Clinicopathological data and laboratory results from within 7 days pre-surgery were extracted. PFS was the primary endpoint. Kaplan-Meier and Cox regression analyses were used to identify independent prognostic factors for model construction. Internal validation was performed via bootstrapping. Model performance was evaluated using the corrected C-index, time-dependent ROC curves, calibration plots, and decision curve analysis (DCA).

During follow-up, 76 PFS events occurred. Compared to the non-event group, patients with events had more advanced FIGO stages, a higher proportion of non-endometrioid histology, and more pronounced inflammatory and hypercoagulable states. Multivariate analysis identified FIGO stage III-IV (HR = 1.95, P = 0.008), non-endometrioid histology (HR = 1.95, P = 0.006), neutrophil-to-lymphocyte ratio (NLR) (HR = 1.15, P = 0.021), and log-transformed cancer antigen 125 (CA125) (HR = 1.79, P = 0.003) as independent predictors. The model showed good discrimination for 3-year PFS (AUC = 0.792), satisfactory calibration, and a positive net benefit on DCA across relevant clinical thresholds.

This prediction model based on readily available clinicopathological and laboratory indicators demonstrated good internal performance for PFS risk stratification in endometrial cancer; however, external validation is required before broader clinical application. NLR and CA125 provide incremental prognostic value beyond traditional staging and pathology, which may aid in personalizing postoperative management.